bambi/models.py
Killed 176 out of 291 mutantsSurvived
Survived mutation testing. These mutants show holes in your test suite.Mutant 261
--- bambi/models.py
+++ bambi/models.py
@@ -56,7 +56,7 @@
auto_scale=True,
dropna=False,
taylor=None,
- noncentered=True,
+ noncentered=False,
):
if isinstance(data, str):
Mutant 270
--- bambi/models.py
+++ bambi/models.py
@@ -69,7 +69,7 @@
self.data = data
# Some random effects stuff later requires us to make guesses about
# column groupings into terms based on patsy's naming scheme.
- if re.search(r"[\[\]]+", "".join(data.columns)):
+ if re.search(r"[\[\]]+", "XXXX".join(data.columns)):
warnings.warn(
"At least one of the column names in the specified "
"dataset contain square brackets ('[' or ']')."
Mutant 271
--- bambi/models.py
+++ bambi/models.py
@@ -79,7 +79,7 @@
)
self.reset()
- self.auto_scale = auto_scale
+ self.auto_scale = None
self.dropna = dropna
self.taylor = taylor
self.noncentered = noncentered
Mutant 273
--- bambi/models.py
+++ bambi/models.py
@@ -81,7 +81,7 @@
self.auto_scale = auto_scale
self.dropna = dropna
- self.taylor = taylor
+ self.taylor = None
self.noncentered = noncentered
self._backend_name = None
Mutant 274
--- bambi/models.py
+++ bambi/models.py
@@ -82,7 +82,7 @@
self.auto_scale = auto_scale
self.dropna = dropna
self.taylor = taylor
- self.noncentered = noncentered
+ self.noncentered = None
self._backend_name = None
# build() will loop over these, calling _add() and _set_priors()
Mutant 275
--- bambi/models.py
+++ bambi/models.py
@@ -83,7 +83,7 @@
self.dropna = dropna
self.taylor = taylor
self.noncentered = noncentered
- self._backend_name = None
+ self._backend_name = ""
# build() will loop over these, calling _add() and _set_priors()
self.added_terms = []
Mutant 279
--- bambi/models.py
+++ bambi/models.py
@@ -91,7 +91,7 @@
# if dropna=True, completes gets updated by add() to track complete cases
self.completes = []
- self.clean_data = None
+ self.clean_data = ""
# attributes that are set later
self.y = None # _add_y()
Mutant 280
--- bambi/models.py
+++ bambi/models.py
@@ -94,7 +94,7 @@
self.clean_data = None
# attributes that are set later
- self.y = None # _add_y()
+ self.y = "" # _add_y()
self.family = None # _add_y()
self.backend = None # _set_backend()
self.dm_statistics = None # build()
Mutant 281
--- bambi/models.py
+++ bambi/models.py
@@ -95,7 +95,7 @@
# attributes that are set later
self.y = None # _add_y()
- self.family = None # _add_y()
+ self.family = "" # _add_y()
self.backend = None # _set_backend()
self.dm_statistics = None # build()
self._diagnostics = None # build()
Mutant 282
--- bambi/models.py
+++ bambi/models.py
@@ -96,7 +96,7 @@
# attributes that are set later
self.y = None # _add_y()
self.family = None # _add_y()
- self.backend = None # _set_backend()
+ self.backend = "" # _set_backend()
self.dm_statistics = None # build()
self._diagnostics = None # build()
self.built = False # build()
Mutant 283
--- bambi/models.py
+++ bambi/models.py
@@ -97,7 +97,7 @@
self.y = None # _add_y()
self.family = None # _add_y()
self.backend = None # _set_backend()
- self.dm_statistics = None # build()
+ self.dm_statistics = "" # build()
self._diagnostics = None # build()
self.built = False # build()
Mutant 284
--- bambi/models.py
+++ bambi/models.py
@@ -98,7 +98,7 @@
self.family = None # _add_y()
self.backend = None # _set_backend()
self.dm_statistics = None # build()
- self._diagnostics = None # build()
+ self._diagnostics = "" # build()
self.built = False # build()
def reset(self):
Mutant 285
--- bambi/models.py
+++ bambi/models.py
@@ -99,7 +99,7 @@
self.backend = None # _set_backend()
self.dm_statistics = None # build()
self._diagnostics = None # build()
- self.built = False # build()
+ self.built = True # build()
def reset(self):
"""Reset list of terms and y-variable."""
Mutant 286
--- bambi/models.py
+++ bambi/models.py
@@ -99,7 +99,7 @@
self.backend = None # _set_backend()
self.dm_statistics = None # build()
self._diagnostics = None # build()
- self.built = False # build()
+ self.built = None # build()
def reset(self):
"""Reset list of terms and y-variable."""
Mutant 288
--- bambi/models.py
+++ bambi/models.py
@@ -104,7 +104,7 @@
def reset(self):
"""Reset list of terms and y-variable."""
self.terms = OrderedDict()
- self.y = None
+ self.y = ""
self.backend = None
self.added_terms = []
self._added_priors = {}
Mutant 289
--- bambi/models.py
+++ bambi/models.py
@@ -105,7 +105,7 @@
"""Reset list of terms and y-variable."""
self.terms = OrderedDict()
self.y = None
- self.backend = None
+ self.backend = ""
self.added_terms = []
self._added_priors = {}
self.completes = []
Mutant 293
--- bambi/models.py
+++ bambi/models.py
@@ -109,7 +109,7 @@
self.added_terms = []
self._added_priors = {}
self.completes = []
- self.clean_data = None
+ self.clean_data = ""
def _set_backend(self, backend):
backend = backend.lower()
Mutant 297
--- bambi/models.py
+++ bambi/models.py
@@ -120,7 +120,7 @@
else:
raise ValueError("At the moment, only the PyMC3 backend is supported.")
- self._backend_name = backend
+ self._backend_name = None
def build(self, backend=None):
"""Set up the model for sampling/fitting.
Mutant 303
--- bambi/models.py
+++ bambi/models.py
@@ -144,7 +144,7 @@
completes = range(len(self.data.index))
self.clean_data = self.data.iloc[list(completes), :]
# warn the user about any dropped rows
- if len(completes) < n_total:
+ if len(completes) <= n_total:
msg = "Automatically removing {}/{} rows from the dataset."
msg = msg.format(n_total - len(completes), n_total)
warnings.warn(msg)
Mutant 304
--- bambi/models.py
+++ bambi/models.py
@@ -145,7 +145,7 @@
self.clean_data = self.data.iloc[list(completes), :]
# warn the user about any dropped rows
if len(completes) < n_total:
- msg = "Automatically removing {}/{} rows from the dataset."
+ msg = "XXAutomatically removing {}/{} rows from the dataset.XX"
msg = msg.format(n_total - len(completes), n_total)
warnings.warn(msg)
Mutant 306
--- bambi/models.py
+++ bambi/models.py
@@ -146,7 +146,7 @@
# warn the user about any dropped rows
if len(completes) < n_total:
msg = "Automatically removing {}/{} rows from the dataset."
- msg = msg.format(n_total - len(completes), n_total)
+ msg = msg.format(n_total + len(completes), n_total)
warnings.warn(msg)
# loop over the added terms and actually _add() them
Mutant 307
--- bambi/models.py
+++ bambi/models.py
@@ -146,7 +146,7 @@
# warn the user about any dropped rows
if len(completes) < n_total:
msg = "Automatically removing {}/{} rows from the dataset."
- msg = msg.format(n_total - len(completes), n_total)
+ msg = None
warnings.warn(msg)
# loop over the added terms and actually _add() them
Mutant 309
--- bambi/models.py
+++ bambi/models.py
@@ -160,7 +160,7 @@
for name, term in self.terms.items():
type_ = (
"intercept"
- if name == "Intercept"
+ if name == "XXInterceptXX"
else "random"
if self.terms[name].random
else "fixed"
Mutant 315
--- bambi/models.py
+++ bambi/models.py
@@ -187,7 +187,7 @@
# X = fixed effects design matrix (excluding intercept/constant term)
# r2_x = 1 - 1/VIF, i.e., R2 for predicting each x from all other x's.
# only compute these stats if there are multiple terms in the model
- terms = [t for t in self.fixed_terms.values() if t.name != "Intercept"]
+ terms = [t for t in self.fixed_terms.values() if t.name != "XXInterceptXX"]
if len(self.fixed_terms) > 1:
Mutant 317
--- bambi/models.py
+++ bambi/models.py
@@ -189,7 +189,7 @@
# only compute these stats if there are multiple terms in the model
terms = [t for t in self.fixed_terms.values() if t.name != "Intercept"]
- if len(self.fixed_terms) > 1:
+ if len(self.fixed_terms) >= 1:
x_matrix = [pd.DataFrame(x.data, columns=x.levels) for x in terms]
x_matrix = pd.concat(x_matrix, axis=1)
Mutant 318
--- bambi/models.py
+++ bambi/models.py
@@ -189,7 +189,7 @@
# only compute these stats if there are multiple terms in the model
terms = [t for t in self.fixed_terms.values() if t.name != "Intercept"]
- if len(self.fixed_terms) > 1:
+ if len(self.fixed_terms) > 2:
x_matrix = [pd.DataFrame(x.data, columns=x.levels) for x in terms]
x_matrix = pd.concat(x_matrix, axis=1)
Mutant 323
--- bambi/models.py
+++ bambi/models.py
@@ -208,7 +208,7 @@
for x in list(x_matrix.columns)
}
),
- "sigma_x": x_matrix.std(),
+ "XXsigma_xXX": x_matrix.std(),
"mean_x": x_matrix.mean(axis=0),
}
Mutant 328
--- bambi/models.py
+++ bambi/models.py
@@ -216,7 +216,7 @@
# mat = correlation matrix of X, w/ diagonal replaced by X means
mat = x_matrix.corr()
for x_col in list(mat.columns):
- mat.loc[x_col, x_col] = self.dm_statistics["mean_x"][x_col]
+ mat.loc[x_col, x_col] = None
self._diagnostics = {
# the Variance Inflation Factors (VIF), which is possibly
# useful for diagnostics
Mutant 329
--- bambi/models.py
+++ bambi/models.py
@@ -220,7 +220,7 @@
self._diagnostics = {
# the Variance Inflation Factors (VIF), which is possibly
# useful for diagnostics
- "VIF": 1 / (1 - self.dm_statistics["r2_x"]),
+ "XXVIFXX": 1 / (1 - self.dm_statistics["r2_x"]),
"corr_mean_X": mat,
}
Mutant 330
--- bambi/models.py
+++ bambi/models.py
@@ -220,7 +220,7 @@
self._diagnostics = {
# the Variance Inflation Factors (VIF), which is possibly
# useful for diagnostics
- "VIF": 1 / (1 - self.dm_statistics["r2_x"]),
+ "VIF": 2 / (1 - self.dm_statistics["r2_x"]),
"corr_mean_X": mat,
}
Mutant 331
--- bambi/models.py
+++ bambi/models.py
@@ -220,7 +220,7 @@
self._diagnostics = {
# the Variance Inflation Factors (VIF), which is possibly
# useful for diagnostics
- "VIF": 1 / (1 - self.dm_statistics["r2_x"]),
+ "VIF": 1 * (1 - self.dm_statistics["r2_x"]),
"corr_mean_X": mat,
}
Mutant 333
--- bambi/models.py
+++ bambi/models.py
@@ -220,7 +220,7 @@
self._diagnostics = {
# the Variance Inflation Factors (VIF), which is possibly
# useful for diagnostics
- "VIF": 1 / (1 - self.dm_statistics["r2_x"]),
+ "VIF": 1 / (1 + self.dm_statistics["r2_x"]),
"corr_mean_X": mat,
}
Mutant 335
--- bambi/models.py
+++ bambi/models.py
@@ -221,7 +221,7 @@
# the Variance Inflation Factors (VIF), which is possibly
# useful for diagnostics
"VIF": 1 / (1 - self.dm_statistics["r2_x"]),
- "corr_mean_X": mat,
+ "XXcorr_mean_XXX": mat,
}
# throw informative error if perfect collinearity among fixed fx
Mutant 337
--- bambi/models.py
+++ bambi/models.py
@@ -225,7 +225,7 @@
}
# throw informative error if perfect collinearity among fixed fx
- if any(self.dm_statistics["r2_x"] > 0.999):
+ if any(self.dm_statistics["r2_x"] >= 0.999):
raise ValueError(
"There is perfect collinearity among the fixed effects!\n"
"Printing some design matrix statistics:\n"
Mutant 338
--- bambi/models.py
+++ bambi/models.py
@@ -225,7 +225,7 @@
}
# throw informative error if perfect collinearity among fixed fx
- if any(self.dm_statistics["r2_x"] > 0.999):
+ if any(self.dm_statistics["r2_x"] > 1.999):
raise ValueError(
"There is perfect collinearity among the fixed effects!\n"
"Printing some design matrix statistics:\n"
Mutant 341
--- bambi/models.py
+++ bambi/models.py
@@ -237,7 +237,7 @@
# throw informative error message if any categorical predictors have 1
# category
num_cats = [x.data.size for x in self.fixed_terms.values()]
- if any(np.array(num_cats) == 0):
+ if any(np.array(num_cats) == 1):
raise ValueError("At least one categorical predictor contains only 1 category!")
# only set priors if there is at least one term in the model
Mutant 344
--- bambi/models.py
+++ bambi/models.py
@@ -246,7 +246,7 @@
if self.taylor is not None:
taylor = self.taylor
else:
- taylor = 5 if self.family.name == "gaussian" else 1
+ taylor = 5 if self.family.name != "gaussian" else 1
scaler = PriorScaler(self, taylor=taylor)
scaler.scale()
Mutant 345
--- bambi/models.py
+++ bambi/models.py
@@ -246,7 +246,7 @@
if self.taylor is not None:
taylor = self.taylor
else:
- taylor = 5 if self.family.name == "gaussian" else 1
+ taylor = 5 if self.family.name == "XXgaussianXX" else 1
scaler = PriorScaler(self, taylor=taylor)
scaler.scale()
Mutant 346
--- bambi/models.py
+++ bambi/models.py
@@ -246,7 +246,7 @@
if self.taylor is not None:
taylor = self.taylor
else:
- taylor = 5 if self.family.name == "gaussian" else 1
+ taylor = 5 if self.family.name == "gaussian" else 2
scaler = PriorScaler(self, taylor=taylor)
scaler.scale()
Mutant 349
--- bambi/models.py
+++ bambi/models.py
@@ -252,7 +252,7 @@
# For bernoulli models with n_trials = 1 (most common use case),
# tell user which event is being modeled
- if self.family.name == "bernoulli" and np.max(self.y.data) < 1.01:
+ if self.family.name != "bernoulli" and np.max(self.y.data) < 1.01:
event = next(i for i, x in enumerate(self.y.data.flatten()) if x > 0.99)
warnings.warn(
"Modeling the probability that {}=='{}'".format(
Mutant 350
--- bambi/models.py
+++ bambi/models.py
@@ -252,7 +252,7 @@
# For bernoulli models with n_trials = 1 (most common use case),
# tell user which event is being modeled
- if self.family.name == "bernoulli" and np.max(self.y.data) < 1.01:
+ if self.family.name == "XXbernoulliXX" and np.max(self.y.data) < 1.01:
event = next(i for i, x in enumerate(self.y.data.flatten()) if x > 0.99)
warnings.warn(
"Modeling the probability that {}=='{}'".format(
Mutant 352
--- bambi/models.py
+++ bambi/models.py
@@ -252,7 +252,7 @@
# For bernoulli models with n_trials = 1 (most common use case),
# tell user which event is being modeled
- if self.family.name == "bernoulli" and np.max(self.y.data) < 1.01:
+ if self.family.name == "bernoulli" and np.max(self.y.data) < 2.01:
event = next(i for i, x in enumerate(self.y.data.flatten()) if x > 0.99)
warnings.warn(
"Modeling the probability that {}=='{}'".format(
Mutant 354
--- bambi/models.py
+++ bambi/models.py
@@ -253,7 +253,7 @@
# For bernoulli models with n_trials = 1 (most common use case),
# tell user which event is being modeled
if self.family.name == "bernoulli" and np.max(self.y.data) < 1.01:
- event = next(i for i, x in enumerate(self.y.data.flatten()) if x > 0.99)
+ event = next(i for i, x in enumerate(self.y.data.flatten()) if x >= 0.99)
warnings.warn(
"Modeling the probability that {}=='{}'".format(
self.y.name, str(self.clean_data[self.y.name].iloc[event])
Mutant 358
--- bambi/models.py
+++ bambi/models.py
@@ -262,7 +262,7 @@
self._set_backend(backend)
self.backend.build(self)
- self.built = True
+ self.built = False
def fit(
self,
Mutant 359
--- bambi/models.py
+++ bambi/models.py
@@ -262,7 +262,7 @@
self._set_backend(backend)
self.backend.build(self)
- self.built = True
+ self.built = None
def fit(
self,
Mutant 363
--- bambi/models.py
+++ bambi/models.py
@@ -318,7 +318,7 @@
family=family,
link=link,
categorical=categorical,
- append=False,
+ append=True,
)
# Run the BackEnd to fit the model.
Mutant 364
--- bambi/models.py
+++ bambi/models.py
@@ -322,7 +322,7 @@
)
# Run the BackEnd to fit the model.
- if backend is None:
+ if backend is not None:
backend = "pymc" if self._backend_name is None else self._backend_name
if run:
Mutant 365
--- bambi/models.py
+++ bambi/models.py
@@ -323,7 +323,7 @@
# Run the BackEnd to fit the model.
if backend is None:
- backend = "pymc" if self._backend_name is None else self._backend_name
+ backend = "XXpymcXX" if self._backend_name is None else self._backend_name
if run:
if not self.built or backend != self._backend_name:
Mutant 366
--- bambi/models.py
+++ bambi/models.py
@@ -323,7 +323,7 @@
# Run the BackEnd to fit the model.
if backend is None:
- backend = "pymc" if self._backend_name is None else self._backend_name
+ backend = "pymc" if self._backend_name is not None else self._backend_name
if run:
if not self.built or backend != self._backend_name:
Mutant 367
--- bambi/models.py
+++ bambi/models.py
@@ -323,7 +323,7 @@
# Run the BackEnd to fit the model.
if backend is None:
- backend = "pymc" if self._backend_name is None else self._backend_name
+ backend = None
if run:
if not self.built or backend != self._backend_name:
Mutant 368
--- bambi/models.py
+++ bambi/models.py
@@ -326,7 +326,7 @@
backend = "pymc" if self._backend_name is None else self._backend_name
if run:
- if not self.built or backend != self._backend_name:
+ if self.built or backend != self._backend_name:
self.build(backend)
return self.backend.run(**kwargs)
Mutant 369
--- bambi/models.py
+++ bambi/models.py
@@ -326,7 +326,7 @@
backend = "pymc" if self._backend_name is None else self._backend_name
if run:
- if not self.built or backend != self._backend_name:
+ if not self.built or backend == self._backend_name:
self.build(backend)
return self.backend.run(**kwargs)
Mutant 370
--- bambi/models.py
+++ bambi/models.py
@@ -326,7 +326,7 @@
backend = "pymc" if self._backend_name is None else self._backend_name
if run:
- if not self.built or backend != self._backend_name:
+ if not self.built and backend != self._backend_name:
self.build(backend)
return self.backend.run(**kwargs)
Mutant 376
--- bambi/models.py
+++ bambi/models.py
@@ -391,7 +391,7 @@
# Explicitly convert columns to category if desired--though this
# can also be done within the formula using C().
- if categorical is not None:
+ if categorical is None:
data = data.copy()
cats = listify(categorical)
data[cats] = data[cats].apply(lambda x: x.astype("category"))
Mutant 384
--- bambi/models.py
+++ bambi/models.py
@@ -409,7 +409,7 @@
dmatrix(fixed, data=data, NA_action=NA_handler)
# screen random terms
- if random is not None:
+ if random is None:
for term in listify(random):
for side in term.split("|"):
dmatrix(side, data=data, NA_action=NA_handler)
Mutant 392
--- bambi/models.py
+++ bambi/models.py
@@ -427,7 +427,7 @@
)
self.added_terms.append(args)
- self.built = False
+ self.built = True
def _add(
self, fixed=None, random=None, priors=None, family="gaussian", link=None, categorical=None,
Mutant 394
--- bambi/models.py
+++ bambi/models.py
@@ -430,7 +430,7 @@
self.built = False
def _add(
- self, fixed=None, random=None, priors=None, family="gaussian", link=None, categorical=None,
+ self, fixed=None, random=None, priors=None, family="XXgaussianXX", link=None, categorical=None,
):
"""Internal version of add(), with the same arguments.
Mutant 397
--- bambi/models.py
+++ bambi/models.py
@@ -439,7 +439,7 @@
# use cleaned data with NAs removed (if user requested)
data = self.clean_data
# alter this pandas flag to avoid false positive SettingWithCopyWarnings
- data._is_copy = False # pylint: disable=protected-access
+ data._is_copy = None # pylint: disable=protected-access
# Explicitly convert columns to category if desired--though this
# can also be done within the formula using C().
Mutant 398
--- bambi/models.py
+++ bambi/models.py
@@ -443,7 +443,7 @@
# Explicitly convert columns to category if desired--though this
# can also be done within the formula using C().
- if categorical is not None:
+ if categorical is None:
data = data.copy()
cats = listify(categorical)
data[cats] = data[cats].apply(lambda x: x.astype("category"))
Mutant 416
--- bambi/models.py
+++ bambi/models.py
@@ -462,7 +462,7 @@
if event is not None:
# pass in new Y data that has 1 if y=event and 0 otherwise
y_data = y_vector[:, y_vector.design_info.column_names.index(event.group(1))]
- y_data = pd.DataFrame({event.group(3): y_data})
+ y_data = pd.DataFrame({event.group(4): y_data})
self._add_y(y_label, family=family, link=link, data=y_data)
else:
# use Y as-is
Mutant 422
--- bambi/models.py
+++ bambi/models.py
@@ -474,7 +474,7 @@
for _name, _slice in x_matrix.design_info.term_name_slices.items():
cols = x_matrix.design_info.column_names[_slice]
term_data = pd.DataFrame(np.asfortranarray(x_matrix[:, _slice]), columns=cols)
- prior = priors.pop(_name, priors.get("fixed", None))
+ prior = priors.pop(_name, priors.get("XXfixedXX", None))
self.terms[_name] = Term(_name, term_data, prior=prior)
# Random effects
Mutant 423
--- bambi/models.py
+++ bambi/models.py
@@ -474,7 +474,7 @@
for _name, _slice in x_matrix.design_info.term_name_slices.items():
cols = x_matrix.design_info.column_names[_slice]
term_data = pd.DataFrame(np.asfortranarray(x_matrix[:, _slice]), columns=cols)
- prior = priors.pop(_name, priors.get("fixed", None))
+ prior = None
self.terms[_name] = Term(_name, term_data, prior=prior)
# Random effects
Mutant 431
--- bambi/models.py
+++ bambi/models.py
@@ -489,7 +489,7 @@
patt = r"^([01]+)*[\s\+]*([^\|]+)*\|(.*)"
intcpt, pred, grpr = re.search(patt, random_effect).groups()
- label = "{}|{}".format(pred, grpr) if pred else grpr
+ label = "XX{}|{}XX".format(pred, grpr) if pred else grpr
prior = priors.pop(label, priors.get("random", None))
# Treat all grouping variables as categoricals, regardless of
Mutant 432
--- bambi/models.py
+++ bambi/models.py
@@ -489,7 +489,7 @@
patt = r"^([01]+)*[\s\+]*([^\|]+)*\|(.*)"
intcpt, pred, grpr = re.search(patt, random_effect).groups()
- label = "{}|{}".format(pred, grpr) if pred else grpr
+ label = None
prior = priors.pop(label, priors.get("random", None))
# Treat all grouping variables as categoricals, regardless of
Mutant 433
--- bambi/models.py
+++ bambi/models.py
@@ -490,7 +490,7 @@
intcpt, pred, grpr = re.search(patt, random_effect).groups()
label = "{}|{}".format(pred, grpr) if pred else grpr
- prior = priors.pop(label, priors.get("random", None))
+ prior = priors.pop(label, priors.get("XXrandomXX", None))
# Treat all grouping variables as categoricals, regardless of
# their dtype and what the user may have specified in the
Mutant 434
--- bambi/models.py
+++ bambi/models.py
@@ -490,7 +490,7 @@
intcpt, pred, grpr = re.search(patt, random_effect).groups()
label = "{}|{}".format(pred, grpr) if pred else grpr
- prior = priors.pop(label, priors.get("random", None))
+ prior = None
# Treat all grouping variables as categoricals, regardless of
# their dtype and what the user may have specified in the
Mutant 435
--- bambi/models.py
+++ bambi/models.py
@@ -495,7 +495,7 @@
# Treat all grouping variables as categoricals, regardless of
# their dtype and what the user may have specified in the
# 'categorical' argument.
- var_names = re.findall(r"(\w+)", grpr)
+ var_names = re.findall(r"XX(\w+)XX", grpr)
for var_name in var_names:
if var_name in data.columns:
data.loc[:, var_name] = data.loc[:, var_name].astype("category")
Mutant 437
--- bambi/models.py
+++ bambi/models.py
@@ -497,7 +497,7 @@
# 'categorical' argument.
var_names = re.findall(r"(\w+)", grpr)
for var_name in var_names:
- if var_name in data.columns:
+ if var_name not in data.columns:
data.loc[:, var_name] = data.loc[:, var_name].astype("category")
self.clean_data.loc[:, var_name] = data.loc[:, var_name]
Mutant 449
--- bambi/models.py
+++ bambi/models.py
@@ -507,7 +507,7 @@
grpr_df = dmatrix(f"0+{grpr}", data, return_type="dataframe", NA_action="raise")
# If there's no predictor, we must be adding random intercepts
- if not pred and grpr not in self.terms:
+ if not pred or grpr not in self.terms:
name = "1|" + grpr
pred = np.ones((len(grpr_df), 1))
term = RandomTerm(
Mutant 450
--- bambi/models.py
+++ bambi/models.py
@@ -508,7 +508,7 @@
# If there's no predictor, we must be adding random intercepts
if not pred and grpr not in self.terms:
- name = "1|" + grpr
+ name = "XX1|XX" + grpr
pred = np.ones((len(grpr_df), 1))
term = RandomTerm(
name, grpr_df, pred, grpr_df.values, categorical=True, prior=prior
Mutant 453
--- bambi/models.py
+++ bambi/models.py
@@ -509,7 +509,7 @@
# If there's no predictor, we must be adding random intercepts
if not pred and grpr not in self.terms:
name = "1|" + grpr
- pred = np.ones((len(grpr_df), 1))
+ pred = np.ones((len(grpr_df), 2))
term = RandomTerm(
name, grpr_df, pred, grpr_df.values, categorical=True, prior=prior
)
Mutant 454
--- bambi/models.py
+++ bambi/models.py
@@ -509,7 +509,7 @@
# If there's no predictor, we must be adding random intercepts
if not pred and grpr not in self.terms:
name = "1|" + grpr
- pred = np.ones((len(grpr_df), 1))
+ pred = None
term = RandomTerm(
name, grpr_df, pred, grpr_df.values, categorical=True, prior=prior
)
Mutant 455
--- bambi/models.py
+++ bambi/models.py
@@ -511,7 +511,7 @@
name = "1|" + grpr
pred = np.ones((len(grpr_df), 1))
term = RandomTerm(
- name, grpr_df, pred, grpr_df.values, categorical=True, prior=prior
+ name, grpr_df, pred, grpr_df.values, categorical=False, prior=prior
)
self.terms[name] = term
else:
Mutant 456
--- bambi/models.py
+++ bambi/models.py
@@ -513,7 +513,7 @@
term = RandomTerm(
name, grpr_df, pred, grpr_df.values, categorical=True, prior=prior
)
- self.terms[name] = term
+ self.terms[name] = None
else:
pred_df = dmatrix(
f"{intcpt}+{pred}", data, return_type="dataframe", NA_action="raise"
Mutant 466
--- bambi/models.py
+++ bambi/models.py
@@ -528,7 +528,7 @@
# Also rename intercepts and skip if already added.
# This can happen if user specifies something like
# random=['1|school', 'student|school'].
- if col == "Intercept":
+ if col != "Intercept":
if grpr in self.terms:
continue
label = f"1|{grpr}"
Mutant 467
--- bambi/models.py
+++ bambi/models.py
@@ -528,7 +528,7 @@
# Also rename intercepts and skip if already added.
# This can happen if user specifies something like
# random=['1|school', 'student|school'].
- if col == "Intercept":
+ if col == "XXInterceptXX":
if grpr in self.terms:
continue
label = f"1|{grpr}"
Mutant 468
--- bambi/models.py
+++ bambi/models.py
@@ -529,7 +529,7 @@
# This can happen if user specifies something like
# random=['1|school', 'student|school'].
if col == "Intercept":
- if grpr in self.terms:
+ if grpr not in self.terms:
continue
label = f"1|{grpr}"
else:
Mutant 471
--- bambi/models.py
+++ bambi/models.py
@@ -533,7 +533,7 @@
continue
label = f"1|{grpr}"
else:
- label = col + "|" + grpr
+ label = col + "XX|XX" + grpr
prior = priors.pop(label, priors.get("random", None))
Mutant 474
--- bambi/models.py
+++ bambi/models.py
@@ -535,7 +535,7 @@
else:
label = col + "|" + grpr
- prior = priors.pop(label, priors.get("random", None))
+ prior = priors.pop(label, priors.get("XXrandomXX", None))
# Categorical or continuous is determined from data
ld_vals = lev_data.values
Mutant 475
--- bambi/models.py
+++ bambi/models.py
@@ -535,7 +535,7 @@
else:
label = col + "|" + grpr
- prior = priors.pop(label, priors.get("random", None))
+ prior = None
# Categorical or continuous is determined from data
ld_vals = lev_data.values
Mutant 477
--- bambi/models.py
+++ bambi/models.py
@@ -539,7 +539,7 @@
# Categorical or continuous is determined from data
ld_vals = lev_data.values
- if ((ld_vals == 0) | (ld_vals == 1)).all():
+ if ((ld_vals != 0) | (ld_vals == 1)).all():
lev_data = lev_data.astype(int)
cat = True
else:
Mutant 478
--- bambi/models.py
+++ bambi/models.py
@@ -539,7 +539,7 @@
# Categorical or continuous is determined from data
ld_vals = lev_data.values
- if ((ld_vals == 0) | (ld_vals == 1)).all():
+ if ((ld_vals == 1) | (ld_vals == 1)).all():
lev_data = lev_data.astype(int)
cat = True
else:
Mutant 479
--- bambi/models.py
+++ bambi/models.py
@@ -539,7 +539,7 @@
# Categorical or continuous is determined from data
ld_vals = lev_data.values
- if ((ld_vals == 0) | (ld_vals == 1)).all():
+ if ((ld_vals == 0) & (ld_vals == 1)).all():
lev_data = lev_data.astype(int)
cat = True
else:
Mutant 480
--- bambi/models.py
+++ bambi/models.py
@@ -539,7 +539,7 @@
# Categorical or continuous is determined from data
ld_vals = lev_data.values
- if ((ld_vals == 0) | (ld_vals == 1)).all():
+ if ((ld_vals == 0) | (ld_vals != 1)).all():
lev_data = lev_data.astype(int)
cat = True
else:
Mutant 481
--- bambi/models.py
+++ bambi/models.py
@@ -539,7 +539,7 @@
# Categorical or continuous is determined from data
ld_vals = lev_data.values
- if ((ld_vals == 0) | (ld_vals == 1)).all():
+ if ((ld_vals == 0) | (ld_vals == 2)).all():
lev_data = lev_data.astype(int)
cat = True
else:
Mutant 483
--- bambi/models.py
+++ bambi/models.py
@@ -541,7 +541,7 @@
ld_vals = lev_data.values
if ((ld_vals == 0) | (ld_vals == 1)).all():
lev_data = lev_data.astype(int)
- cat = True
+ cat = False
else:
cat = False
Mutant 484
--- bambi/models.py
+++ bambi/models.py
@@ -541,7 +541,7 @@
ld_vals = lev_data.values
if ((ld_vals == 0) | (ld_vals == 1)).all():
lev_data = lev_data.astype(int)
- cat = True
+ cat = None
else:
cat = False
Mutant 485
--- bambi/models.py
+++ bambi/models.py
@@ -543,7 +543,7 @@
lev_data = lev_data.astype(int)
cat = True
else:
- cat = False
+ cat = True
pred_data = pred_data[:, None] # Must be 2D later
term = RandomTerm(
Mutant 486
--- bambi/models.py
+++ bambi/models.py
@@ -543,7 +543,7 @@
lev_data = lev_data.astype(int)
cat = True
else:
- cat = False
+ cat = None
pred_data = pred_data[:, None] # Must be 2D later
term = RandomTerm(
Mutant 489
--- bambi/models.py
+++ bambi/models.py
@@ -558,7 +558,7 @@
self.terms[label] = term
# pylint: disable=keyword-arg-before-vararg
- def _add_y(self, variable, prior=None, family="gaussian", link=None, *args, **kwargs):
+ def _add_y(self, variable, prior=None, family="XXgaussianXX", link=None, *args, **kwargs):
"""Add a dependent (or outcome) variable to the model.
Parameters
Mutant 499
--- bambi/models.py
+++ bambi/models.py
@@ -594,7 +594,7 @@
prior = self.family.prior
if self.family.name == "gaussian":
- prior.update(sigma=Prior("HalfStudentT", nu=4, sigma=self.clean_data[variable].std()))
+ prior.update(sigma=Prior("HalfStudentT", nu=5, sigma=self.clean_data[variable].std()))
data = kwargs.pop("data", self.clean_data[variable])
term = Term(variable, data, prior=prior, *args, **kwargs)
Mutant 504
--- bambi/models.py
+++ bambi/models.py
@@ -599,7 +599,7 @@
data = kwargs.pop("data", self.clean_data[variable])
term = Term(variable, data, prior=prior, *args, **kwargs)
self.y = term
- self.built = False
+ self.built = True
def _match_derived_terms(self, name):
"""Return all (random) terms whose named are derived from the specified string.
Mutant 505
--- bambi/models.py
+++ bambi/models.py
@@ -599,7 +599,7 @@
data = kwargs.pop("data", self.clean_data[variable])
term = Term(variable, data, prior=prior, *args, **kwargs)
self.y = term
- self.built = False
+ self.built = None
def _match_derived_terms(self, name):
"""Return all (random) terms whose named are derived from the specified string.
Mutant 506
--- bambi/models.py
+++ bambi/models.py
@@ -628,7 +628,7 @@
# have been set by some other specification like 'gender|subject').
return found if found and (len(found) > 1 or found[0].name != intcpt) else None
- def set_priors(self, priors=None, fixed=None, random=None, match_derived_names=True):
+ def set_priors(self, priors=None, fixed=None, random=None, match_derived_names=False):
"""Set priors for one or more existing terms.
Parameters
Mutant 507
--- bambi/models.py
+++ bambi/models.py
@@ -661,7 +661,7 @@
self.built = False
- def _set_priors(self, priors=None, fixed=None, random=None, match_derived_names=True):
+ def _set_priors(self, priors=None, fixed=None, random=None, match_derived_names=False):
"""Internal version of set_priors(), with same arguments.
Runs during Model.build().
Mutant 509
--- bambi/models.py
+++ bambi/models.py
@@ -668,7 +668,7 @@
"""
targets = {}
- if fixed is not None:
+ if fixed is None:
targets.update({name: fixed for name in self.fixed_terms.keys()})
if random is not None:
Mutant 510
--- bambi/models.py
+++ bambi/models.py
@@ -671,7 +671,7 @@
if fixed is not None:
targets.update({name: fixed for name in self.fixed_terms.keys()})
- if random is not None:
+ if random is None:
targets.update({name: random for name in self.random_terms.keys()})
if priors is not None:
Mutant 512
--- bambi/models.py
+++ bambi/models.py
@@ -700,7 +700,7 @@
_type : string
accepted values are: 'intercept, 'fixed', or 'random'.
"""
- if prior is None and not self.auto_scale:
+ if prior is not None and not self.auto_scale:
prior = self.default_priors.get(term=_type + "_flat")
if isinstance(prior, Prior):
Mutant 513
--- bambi/models.py
+++ bambi/models.py
@@ -700,7 +700,7 @@
_type : string
accepted values are: 'intercept, 'fixed', or 'random'.
"""
- if prior is None and not self.auto_scale:
+ if prior is None and self.auto_scale:
prior = self.default_priors.get(term=_type + "_flat")
if isinstance(prior, Prior):
Mutant 514
--- bambi/models.py
+++ bambi/models.py
@@ -700,7 +700,7 @@
_type : string
accepted values are: 'intercept, 'fixed', or 'random'.
"""
- if prior is None and not self.auto_scale:
+ if prior is None or not self.auto_scale:
prior = self.default_priors.get(term=_type + "_flat")
if isinstance(prior, Prior):
Mutant 515
--- bambi/models.py
+++ bambi/models.py
@@ -706,7 +706,7 @@
if isinstance(prior, Prior):
prior._auto_scale = False # pylint: disable=protected-access
else:
- _scale = prior
+ _scale = None
prior = self.default_priors.get(term=_type)
prior.scale = _scale
if prior.scale is not None:
Mutant 517
--- bambi/models.py
+++ bambi/models.py
@@ -708,7 +708,7 @@
else:
_scale = prior
prior = self.default_priors.get(term=_type)
- prior.scale = _scale
+ prior.scale = None
if prior.scale is not None:
prior._auto_scale = False # pylint: disable=protected-access
return prior
Mutant 518
--- bambi/models.py
+++ bambi/models.py
@@ -709,7 +709,7 @@
_scale = prior
prior = self.default_priors.get(term=_type)
prior.scale = _scale
- if prior.scale is not None:
+ if prior.scale is None:
prior._auto_scale = False # pylint: disable=protected-access
return prior
Mutant 524
--- bambi/models.py
+++ bambi/models.py
@@ -792,7 +792,7 @@
treated as an intercept for prior distribution purposes.
"""
- random = False
+ random = None
def __init__(self, name, data, categorical=False, prior=None, constant=None):
self.name = name
Mutant 525
--- bambi/models.py
+++ bambi/models.py
@@ -794,7 +794,7 @@
random = False
- def __init__(self, name, data, categorical=False, prior=None, constant=None):
+ def __init__(self, name, data, categorical=True, prior=None, constant=None):
self.name = name
self.categorical = categorical
self._reduced_data = None
Mutant 528
--- bambi/models.py
+++ bambi/models.py
@@ -797,7 +797,7 @@
def __init__(self, name, data, categorical=False, prior=None, constant=None):
self.name = name
self.categorical = categorical
- self._reduced_data = None
+ self._reduced_data = ""
if isinstance(data, pd.Series):
data = data.to_frame()
Mutant 535
--- bambi/models.py
+++ bambi/models.py
@@ -815,7 +815,7 @@
# identify and flag intercept and cell-means terms (i.e., full-rank
# dummy codes), which receive special priors
if constant is None:
- self.constant = np.atleast_2d(data.T).T.sum(1).var() == 0
+ self.constant = np.atleast_2d(data.T).T.sum(1).var() != 0
else:
self.constant = constant
Mutant 536
--- bambi/models.py
+++ bambi/models.py
@@ -815,7 +815,7 @@
# identify and flag intercept and cell-means terms (i.e., full-rank
# dummy codes), which receive special priors
if constant is None:
- self.constant = np.atleast_2d(data.T).T.sum(1).var() == 0
+ self.constant = np.atleast_2d(data.T).T.sum(1).var() == 1
else:
self.constant = constant
Mutant 537
--- bambi/models.py
+++ bambi/models.py
@@ -815,7 +815,7 @@
# identify and flag intercept and cell-means terms (i.e., full-rank
# dummy codes), which receive special priors
if constant is None:
- self.constant = np.atleast_2d(data.T).T.sum(1).var() == 0
+ self.constant = None
else:
self.constant = constant
Mutant 539
--- bambi/models.py
+++ bambi/models.py
@@ -819,7 +819,7 @@
else:
self.constant = constant
- self.prior = prior
+ self.prior = None
class RandomTerm(Term):
Mutant 542
--- bambi/models.py
+++ bambi/models.py
@@ -826,7 +826,7 @@
random = True
def __init__(
- self, name, data, predictor, grouper, categorical=False, prior=None, constant=None
+ self, name, data, predictor, grouper, categorical=True, prior=None, constant=None
):
super(RandomTerm, self).__init__(name, data, categorical, prior, constant)
Mutant 543
--- bambi/models.py
+++ bambi/models.py
@@ -830,7 +830,7 @@
):
super(RandomTerm, self).__init__(name, data, categorical, prior, constant)
- self.grouper = grouper
+ self.grouper = None
self.predictor = predictor
self.group_index = self.invert_dummies(grouper)
Mutant 547
--- bambi/models.py
+++ bambi/models.py
@@ -841,7 +841,7 @@
dummies as a vector of indices into the coefficients.
"""
vec = np.zeros(len(dummies), dtype=int)
- for i in range(1, dummies.shape[1]):
+ for i in range(2, dummies.shape[1]):
vec[dummies[:, i] == 1] = i
return vec
Mutant 549
--- bambi/models.py
+++ bambi/models.py
@@ -842,6 +842,6 @@
"""
vec = np.zeros(len(dummies), dtype=int)
for i in range(1, dummies.shape[1]):
- vec[dummies[:, i] == 1] = i
+ vec[dummies[:, i] != 1] = i
return vec
Mutant 550
--- bambi/models.py
+++ bambi/models.py
@@ -842,6 +842,6 @@
"""
vec = np.zeros(len(dummies), dtype=int)
for i in range(1, dummies.shape[1]):
- vec[dummies[:, i] == 1] = i
+ vec[dummies[:, i] == 2] = i
return vec